Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19.

Background: As of 30 April 2023, the COVID-19 pandemic has resulted in over 6.9 million deaths worldwide. The virus continues to spread and mutate, leading to continuously evolving pathological and physiological processes. It is imperative to reevaluate predictive factors for identifying the risk of early disease progression. Methods: A retrospective study was conducted on a cohort of 1379 COVID-19 patients who were discharged from Xin Hua Hospital affiliated with Shanghai Jiao Tong University School of Medicine between 15 December 2022 and 15 February 2023. Patient symptoms, comorbidities, demographics, vital signs, and laboratory test results were systematically documented. The dataset was split into testing and training sets, and 15 different machine learning algorithms were employed to construct prediction models. These models were assessed for accuracy and area under the receiver operating characteristic curve (AUROC), and the best-performing model was selected for further analysis. Results: AUROC for models generated by 15 machine learning algorithms all exceeded 90%, and the accuracy of 10 of them also surpassed 90%. Light Gradient Boosting model emerged as the optimal choice, with accuracy of 0.928 ± 0.0006 and an AUROC of 0.976 ± 0.0028. Notably, the factors with the greatest impact on in-hospital mortality were growth stimulation expressed gene 2 (ST2,19.3%), interleukin-8 (IL-8,17.2%), interleukin-6 (IL-6,6.4%), age (6.1%), NT-proBNP (5.1%), interleukin-2 receptor (IL-2R, 5%), troponin I (TNI,4.6%), congestive heart failure (3.3%) in Light Gradient Boosting model. Conclusion: ST-2, IL-8, IL-6, NT-proBNP, IL-2R, TNI, age and congestive heart failure were significant predictors of in-hospital mortality among COVID-19 patients.

Understanding the effect of foreign atoms occupation on the metamagnetic behaviors in MnCoSi-based alloys: taking Pt-doping as an example.

Present research on TiNiSi-type MnCoSi-based alloys focuses on finding a suitable doping element to effectively reduce the critical magnetic field (µ0Hcri) required to induce a metamagnetic transition. This paper provides a guide to achieve this goal through an experimental investigation of Mn1-xPtxCoSi and MnCo1-xPtxSi alloys. In Mn1-xPtxCoSi, asxincreases,µ0Hcriat room temperature decreases, while in MnCo1-xPtxSi, it increases. This phenomenon can be attributed to the fact that larger Pt atoms prefer Co sites over Mn sites, as predicted by our density-functional theory. Consequently, in Mn1-xPtxCoSi, larger Co atoms are extruded into the Mn atoms chain, increasing the nearest Mn-Mn distance and resulting in a reducedµ0Hcri. This finding suggests that transition-metal atoms with more valence electrons preferably occupy the Co site, while those with fewer valence electrons preferably occupy the Mn site. Adhering to this rule, one can easily obtain a lowµ0Hcriand large magnetostrain under a low magnetic field by selecting a suitable foreign element and chemical formula, as demonstrated by the Mn1-xPtxCoSi alloy.

Myofibrillogenesis Regulator-1 Regulates the Ubiquitin Lysosomal Pathway of Notch3 Intracellular Domain Through E3 Ubiquitin-Protein Ligase Itchy Homolog in the Metastasis of Non-Small Cell Lung Cancer.

Myofibrillogenesis regulator-1 (MR-1) is a multifunctional protein involved in the development of various human tumors. The study is the first to report the promoting effect of MR-1 on the development and metastasis of non-small cell lung cancer (NSCLC). MR-1 is upregulated in NSCLC and positively associated with poor prognosis. The overexpression of MR-1 promotes the metastasis of NSCLC cells by stabilizing the expression of Notch3-ICD (NICD3) in the cytoplasm through enrichment analysis, in vitro and in vivo experimental researches. And Notch3 signaling can upregulate many genes related to metastasis. The stabilizing effect of MR-1 on NICD3 is achieved through the mono-ubiquitin lysosomal pathway and the specific E3 ubiquitin ligase is Itchy homolog (ITCH). There is a certain interaction between MR-1 and NICD3. Elevated MR-1 can affect the level of ITCH phosphorylation, reduce its E3 enzyme activity, and thus lead to reduce the ubiquitination and degradation of NICD3. Interference with the interaction between MR-1 and NICD3 can increase the degradation of NICD3 and impair the metastatic ability of NSCLC cells, which is a previously overlooked treatment option in NSCLC. In summary, interference with the interaction between MR-1 and NICD3 in the progression of lung cancer may be a promising therapeutic target.

Financial crisis early warning of Chinese listed companies based on MD&A text-linguistic feature indicators.

Nowadays, the international situation is severe and complex, and the structural issues within the Chinese economy are prominent. Consequently, the financial risks faced by Chinese listed companies continue to escalate. Hence, it is of great practical significance to furnish effective early warnings for financial crises in listed companies. In this paper, we first employ web crawler technology and natural language processing technology to assess four text-linguistic features in the Management Discussion and Analysis (MD&A) section of the annual financial reports of listed companies in China from 2011 to 2020. These features are text tone, forward-looking, readability and similarity. Subsequently, we combine these features with traditional financial indicators and explore thirteen mainstream machine learning models to comparatively analyze their effectiveness in predicting financial crises in listed companies. The empirical findings of this research reveal that MD&A text readability and similarity indicators contribute valuable incremental information to prediction models, whereas text tone and forward-looking indicators exhibit the opposite effect. The latter two indicators can be manipulated more effortlessly by management, as this study's empirical findings indicate no evidence of their contributions in incremental informational value. In fact, the forward-looking indicator even introduces additional noise to the prediction. Finally, by comparing the early warning effects of thirteen machine learning models, it is found that RF, Bagging, CatBoost, GBDT, XGBoost and LightGBM models maintain stable and accurate sample recognition ability. In general, this paper constructs a more effective financial crisis early warning model by exploring the MD&A text-linguistic feature indicators, thereby offering a fresh research perspective for further investigations in this field.

Mapping the knowledge domain of financial decision making: A scientometric and bibliometric study.

Based on a 12-year bibliographic record collected from the Web of Science (Thomson Reuters) database, the present study aims to provide a macroscopic overview of the knowledge domain in financial decision making (FDM). A scientometric and bibliometric analysis was conducted on the literature published in the field from 2010 to 2021, using the CiteSpace software. The analysis focuses on the co-occurring categories, the geographic distributions, the vital references, the distribution of topics, as well as the research fronts and emerging trends of financial related decision making. The steady increase of papers published year by year demonstrated the increasing interest on this topic at the international level. The scientometric analysis of the literature showed that financial decision, investment decision, and financing decision stood out of the crowd of the research on FDM, suggesting their important role in FDM and its research. The results of citation burst analysis predicted the focus of topics, i.e., the impact of individual differences such as financial literacy, gender and age on FDM in the coming years. Different from the traditional approach of literature review, this bibliometric analysis offers a scientometric approach to reveal the status quo and the development trend of FDM by macro and quantitative means. In addition, future research directions for the field are recommended.

The bHLH1-DTX35/DFR module regulates pollen fertility by promoting flavonoid biosynthesis in Capsicum annuum L.

High pollen fertility can ensure the yield and efficiency of breeding work, but factors that affect the fertility of pepper pollen have not been studied extensively. In this work, we screened the reduced pollen fertility 1 (rpf1) mutant of Capsicum annuum with reduced pollen fertility and yellow anthers from an EMS (ethyl methanesulfonate)-mutagenized pepper population. Through construction of an F 2 population followed by BSA (bulked segregant analysis) mapping and KASP genotyping, we identified CabHLH1 as a candidate gene for control of this trait. A G â†’ A mutation at a splice acceptor site in CabHLH1 causes a frameshift mutation in the mutant, and the translated protein is terminated prematurely. Previous studies on CabHLH1 have focused on the regulation of flavonoid synthesis. Here, we found that CabHLH1 also has an important effect on pollen fertility. Pollen vigor, anther flavonoid content, and seed number were lower in CabHLH1-silenced pepper plants, whereas anther H2O2 and MDA (malondialdehyde) contents were higher. RNA-seq analyses showed that expression of the flavonoid synthesis genes DFR, ANS, and RT was significantly reduced in anthers of CabHLH1-silenced plants and rpf1 plants, as was the expression of DTX35, a gene related to pollen fertility and flavonoid transport. Yeast one-hybrid and dual-luciferase reporter assays showed that CabHLH1 can directly bind to the promoters of DTX35 and DFR and activate their expression. These results indicate that CabHLH1 regulates reactive oxygen species homeostasis by promoting the synthesis of anther flavonoids and acts as a positive regulator of pepper pollen fertility.

Genome-Wide Analysis of the MYB-Related Transcription Factor Family in Pepper and Functional Studies of CaMYB37 Involvement in Capsaicin Biosynthesis.

Chili pepper is an important economic vegetable worldwide. MYB family gene members play an important role in the metabolic processes in plant growth and development. In this study, 103 pepper MYB-related members were identified and grouped into nine subfamilies according to phylogenetic relationships. Additionally, a total of 80, 20, and 37 collinear gene pairs were identified between pepper and tomato, pepper and Arabidopsis, and tomato and Arabidopsis, respectively. We performed promoter cis-element analysis and showed that CaMYB-related members may be involved in multiple biological processes such as growth and development, secondary metabolism, and circadian rhythm regulation. Expression pattern analysis indicated that CaMYB37 is significantly more enriched in fruit placenta, suggesting that this gene may be involved in capsaicin biosynthesis. Through VIGS, we confirmed that CaMYB37 is critical for the biosynthesis of capsaicin in placenta. Our subcellular localization studies revealed that CaMYB37 localized in the nucleus. On the basis of yeast one-hybrid and dual-luciferase reporter assays, we found that CaMYB37 directly binds to the promoter of capsaicin biosynthesis gene AT3 and activates its transcription, thereby regulating capsaicin biosynthesis. In summary, we systematically identified members of the CaMYB-related family, predicted their possible biological functions, and revealed that CaMYB37 is critical for the transcriptional regulation of capsaicin biosynthesis. This work provides a foundation for further studies of the CaMYB-related family in pepper growth and development.

Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning.

Addressing global climate change has become a broad consensus in the international community. Low-carbon economic development, as an effective means to address global climate change issues, has been widely explored and practiced by countries around the world. As major carbon emitting countries, there has been much focus on China, Japan and South Korea, and it is of practical significance to study their low-carbon economic development. To further measure their trend of low-carbon economic development, this paper firstly constructs a low-carbon economic efficiency evaluation index system and uses the Slack Based Measure (SBM) model. This is a kind of data envelopment analysis (DEA) method, with undesirable output based on global covariance to measure the low-carbon economic efficiency of 94 provincial-level administrative divisions (PLADs) in China, Japan, and South Korea from 2013 to 2019. Subsequently, this paper uses 10 mainstream machine learning models and combining them with Grid Search with Cross Validation (GridSearchCV) methods, selects the machine learning model with the best prediction effect. The model predicts the low-carbon economic efficiency of PLADs in China, Japan, and South Korea from 2020 to 2024 based on the parameter configuration for the best prediction effect. Finally, according to the research results, this paper proposes targeted advice for regionalized cooperation on low-carbon economic development in China, Japan, and South Korea to jointly address global climate change issues.

YOD1 serves as a potential prognostic biomarker for pancreatic cancer.

BACKGROUND: Ubiquitination is a basic post-translational modification of intracellular proteins and can be reversed enzymatically by DUBs (deubiquitinating enzymes). More than 90 DUBs have been identified. Among them, the deubiquitinating enzyme YOD1, a member of the ovarian tumor domain protease (OTUs) subfamily, is involved in the regulation of endoplasmic reticulum (ER)-related degradation pathways. In fact, it is reported that YOD1 is an important proliferation and metastasis-inducing gene, which can stimulate the characteristics of cancer stem cells and maintain circulating tumor cells (CTC). However, the expression level, prognostic effect and biological functional mechanism of YOD1 in pancreatic cancer are still unclear. RESULTS: In the GEO and TCGA databases, YOD1 mRNA expression is significantly up regulated in a variety of human pancreatic cancer tissues. Survival analysis showed that the up regulation of YOD1 can predict poor prognosis of pancreatic cancer. Cox analysis showed that high YOD1 expression is an independent prognostic factor of pancreatic cancer. ROC analysis shows that YOD1 has significant diagnostic value. The immunohistochemistry (IHC) results showed that the protein expression level of YOD1 in pancreatic cancer tissue was higher than that in neighboring non-pancreatic cancer tissues (P < 0.001). In addition, we found that YOD1 expression is negatively correlated with the infiltration level of CD8 + T cells, macrophages, neutrophils and dendritic cells (DC) in pancreatic cancer. The expression of YOD1 has a strong correlation with the different immune marker sets in PAAD. Co-expression network and functional enrichment analysis indicate that YOD1 may participate in the development of pancreatic cancer through cell adhesion molecules, p53, Hippo, TGF-ß and other pathways. The experimental results of EDU, Transwell, Immunohistochemistry (IHC), Western blot and Flow Cytometry indicate that YOD1 is highly expressed in pancreatic cancer cells and pancreatic cancer tissues, and its overexpression can promote the proliferation and metastasis of pancreatic cancer cells and affect the immune microenvironment. CONCLUSION: Our results indicate that YOD1 may be a useful biomarker for the prognosis of human pancreatic cancer, and it may also be a potential molecular target for the diagnosis and treatment of pancreatic cancer.

A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage SBM-Undesirable Model and an LSTM Model.

As a major carbon-emitting country, there is an urgent need for China to reduce carbon emissions. Studying the carbon emission efficiency of each province helps us to learn about the characteristics and evolution of regional carbon emissions, which is important for proposing effective and targeted measures to achieve the carbon peaking and carbon neutrality goals. This paper measures the carbon emission efficiency of 30 Chinese provinces from 2006 to 2019 based on a three-stage SBM-undesirable model and explores external drivers using stochastic frontier models. The results of the SBM-undesirable model show that the inter-provincial carbon emission efficiency is unevenly distributed and shows a big difference. From the results of the stochastic frontier model analysis, external drivers such as the intensity of finance in environmental protection, the level of economic development, the industrial structure, the level of urbanization, the degree of openness and the level of science as well as technology innovation all have an impact on the emission efficiency. In terms of LSTM model prediction, the model shows an excellent fitting effect, which provides a possible path for carbon emission efficiency prediction. Finally, based on the empirical results and the actual situation of each province in China, this paper proposes relevant feasible suggestions.

Genome-Wide Identification and Characterization of the bHLH Transcription Factor Family in Pepper (Capsicum annuum L.).

Plant basic helix-loop-helix (bHLH) transcription factors are involved in the regulation of various biological processes in plant growth, development, and stress response. However, members of this important transcription factor family have not been systematically identified and analyzed in pepper (Capsicum annuum L.). In this study, we identified 122 CabHLH genes in the pepper genome and renamed them based on their chromosomal locations. CabHLHs were divided into 21 subfamilies according to their phylogenetic relationships, and genes from the same subfamily had similar motif compositions and gene structures. Sixteen pairs of tandem and segmental duplicated genes were detected in the CabHLH family. Cis-elements identification and expression analysis of the CabHLHs revealed that they may be involved in plant development and stress responses. This study is the first comprehensive analysis of the CabHLH genes and will serve as a reference for further characterization of their molecular functions.

Controllable Negative Thermal Expansion by Mechanical Pulverizing in Hexagonal Mn0.965Co1.035Ge Compounds.

Negative thermal expansion (NTE) material as a compensator is very important for accurately controlling the thermal expansion of materials. Along with the magnitude of the coefficient of thermal expansion, the operating temperature window of the NTE materials is also a major concern. However, only a few of the NTE materials possess both a large operating temperature range and a large thermal expansion coefficient. To explore this type of new NTE material, the Mn0.965Co1.035Ge fine powders were prepared by mechanical ball milling (BM). These fine powders show a largely extended NTE operation temperature window simultaneously possessing a giant thermal expansion coefficient. For samples treated with different BM times, such as the BM-0.5h, BM-4h, and BM-12 h samples, the operating temperature window (Δ T) and linear thermal expansion coefficient (αL) are 167 K (222-389 K) and ∼ -63 ppm/K, 221 K (140-360 K) and ∼ -41.3 ppm/K, and 208 K (234-442 K) and ∼ -40 ppm/K, respectively, which are larger than most well-known NTE materials. More strikingly, all BM samples have a large constant linear NTE coefficient with an ultrawide temperature window covering room temperature. For these three samples, these values are ∼ -52 ppm/K (140 K), ∼ -58.3 ppm/K (110 K), and ∼ -65 ppm/K (80 K), respectively. The origin of the excellent NTE properties is discussed based on the thermomagnetic measurements and X-ray absorption spectroscopic results.

Lipidomic study reveals the effect of morphological variation and other metabolite interactions on the lipid composition in various cultivars of Bok choy.

Bok choy is an important Brassica vegetable which is also known for its wide range of cultivars that differ in their appearance, leaf color, size and shape. For the purpose to investigate the effect of these phenotypic differences on their lipid composition, seven morphotypes of NHCC (Suzhouqing, Aijaohuang, Wutacai, Yellowrose, Ziluolan, Xiangqingcai and Zicaitai) were selected for this study. For this reason, extensive metabolic approach was adopted which was mainly focused on lipidomics. The overall metabolic position of lipids was determined and the isolated lipid compounds were characterized on the basis of their lipid classes. Moreover, discriminative analysis was applied to monitor the distribution pattern of lipid in different cultivars. Aijiaohuang was the leading cultivar which contained highest lipid levels, whereas least proportion was found in Zicaitai. We proposed that leaf color might have an effect on the lipid composition such as purple cultivars were dominated in glycerophopholipids, light green in fatty acids and dark green were rich in glycerolipids. The level of metabolites differed greatly among different genotypes. Lipid-metabolite interactions revealed the positive correlation of lipids with flavonoid and hydroxycinnamoyl derivatives, whereas negative correlation was noticed in case of phenylamines. This is the first comprehensive study based on lipidomics in order to evaluate the substantial impact of various phenotypes on the metabolic composition of NHCC.